Owing to rapid development of the monitoring industry in recent years, monitoring video cameras can be found almost everywhere in public places. As the number of monitoring video cameras increases, the demands for tracking across different video cameras also increase. When a tracked object disappears from a monitoring range of an original video camera, a further determination needs to be made to detect whether the tracked object has left the public place where the original video camera is deployed or appears in a monitoring range of a different video camera. For example, if a consumer in a store disappears from a monitoring range of a first video camera, a further determination needs to be made to detect whether this consumer has moved into a monitoring range of a second video camera or has left the store.
For the problem described above, a common solution is to detect all figures in a monitoring frame according to a figure detection method, and then retrieve features from figure images for comparison. However, this consumes a lot of computing resources, and is disadvantageous in that it is difficult to distinguish between figures having similar features. Accordingly, a monitoring range map showing overlapped regions among video cameras may be created by comparing stationary objects in the video cameras and with the assistance of position information. For example, if deployment positions of other video cameras having monitoring ranges overlapped with first video camera are known, then positions where the consumer possibly shows up can be predicted to significantly reduce the comparison range. The most common and effective way to compare stationary objects is to find the so-called feature points in the graphics, and if a common feature point can be found through comparison in monitoring ranges of two video cameras, image mapping can be created between images captured by the two video cameras to create position mapping.
However, the aforesaid conventional way to find feature points assumes that there is no or only little non-linear distortion in the images captured by the video cameras. Therefore, when this conventional way is applied to non-linear distortion lenses such as wide-angle lenses or fisheye lenses, misjudgement of common feature points within monitoring ranges of a plurality of video cameras often takes place or positions of the common feature points cannot be mapped correctly to make it impossible to accurately track the object. Therefore, there is difficulty in practical implementation of this conventional method.
In terms of feature point searching in case of non-linear distortion in conventional graphics, early researches mainly assume that the non-linear distortion can be eliminated by a perfect projection formula. However, as has been proved later, it is difficult to generate a perfect projection formula unless manual or semi-automatic distortion measurement is made by use of a black-and-white checkerboard image.
In recent years, practices that reduce the measurement demands as far as possible and search for feature points on a distorted image directly have also been proposed, but most of them have limitations. For example, some of them assume that the non-linear distortion follows an invariable model. However, because there are various models of wide-angle lenses and fisheye lenses, the aforesaid assumption is still not a perfect common solution. Position matching for wide-angle lenses and fisheye lenses still remains as a difficult problem at present because it is difficult to stably find feature points. The non-linear distortion applied by these kinds of lenses leads to distortion of the conventional feature points per se and feature descriptors that describe surrounding relationships of the feature points, and this may cause failure of the matching. And when two video cameras have a large distance therebetween, adverse influences will come from not only the non-linear distortion of the video cameras per se but also from different viewing angles (e.g., a front view and a back view of the object).
The objective of the present invention is to solve the problem of position mapping in multiple non-linear distortion lenses in a fully automatic manner and particularly in cases of wide-angle and fisheye lenses.